Research On Vehicle Detection Technology In Intelligent Inspection Of "Three Span" Transmission Line

With the development of China’s electric power industry and its increasing importance in the national economic construction and life,it is very important to ensure the safe and stable operation of the power system" The "three span" transmission line across expressways,high-speed railways and important transmission channels is a key part of power grid construction.Monitoring the construction vehicles that may cause external damage to the transmission line in the construction area around the line can timely warn risks and prevent safety accidents.For the high-speed railway across the transmission line,monitoring the train running through this section can determine whether the operation of the power system along the line is stable.In addition,the statistics of the traffic flow of the expressway across the transmission line can provide powerful external traffic data for the construction of the safety system of transmission equipment and ensure the safe operation of the power system.In view of the above purpose,this paper proposes to use convolution neural network for intelligent detection of vehicles in "three span" transmission lines,automatically extract target features,and improve the accuracy of vehicle recognition and location.On the basis of vehicle detection of "three span" transmission line crossing expressway section,this paper combined with moving object state prediction algorithm to achieve multi-target tracking of vehicles in the video,determine the trajectory of each vehicle,and then achieve the purpose of accurate traffic statistics.Based on this,this paper focuses on the following four aspects of research and Innovation:first,for the "three span" transmission line vehicle detection task,self built data sets including seven vehicle categories of engineering vehicles and high-speed trains.Aiming at the problem of low brightness of transmission line monitoring image,target vehicle and background are mixed,and it is difficult to extract effective features,an improved histogram equalization algorithm is used to enhance the image,improve the overall brightness and contrast of the image,and enhance the effective detail features of the target.Secondly,one-stage target detection network yolov5 is used to automatically extract vehicle features in transmission line to realize target recognition and location.In the framework of yolov5 network,the non local module is introduced to correlate the global feature information in the feature map,obtain a wider range of semantic feature distribution in the output feature map,strengthen the feature points favorable to the detection task,suppress the background noise and other adverse factors,and improve the accuracy of multi-scale Engineering vehicle detection in the "three span" transmission line.Thirdly,in the task of highway traffic flow statistics,the lightweight module ghost block is constructed to improve the network framework of yolov5,which reduces the parameters and computational complexity of highway vehicle detection model.Vehicle detection is combined with Kalman filter algorithm to achieve multi-target vehicle tracking,and the virtual count line is demarcated in the video to achieve real-time and real-time The effect of accurate statistics of traffic flow.Fourthly,the software platform of vehicle detection and traffic flow statistics in "three span" transmission line is designed and built,which adopts B/S architecture and is based on Python language and Django application framework.According to different functional requirements,combined with the corresponding algorithm content,two modules of vehicle detection and traffic flow statistics are constructed to visually display the effect of vehicle detection and traffic flow statistics.